TASK #1: UNDERSTAND THE PROBLEM STATEMENT AND BUSINESS CASE¶

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In [ ]:

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Data Source: https://www.kaggle.com/arjunbhasin2013/ccdata

TASK #2: IMPORT LIBRARIES AND DATASETS¶

In [85]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.preprocessing import StandardScaler, normalize
from sklearn.cluster import KMeans
from sklearn.decomposition import PCA
from jupyterthemes import jtplot
jtplot.style(theme='monokai', context='notebook', ticks=True, grid=False)
# setting the style of the notebook to be monokai theme  
# this line of code is important to ensure that we are able to see the x and y axes clearly
# If you don't run this code line, you will notice that the xlabel and ylabel on any plot is black on black and it will be hard to see them.
In [86]:
# You have to include the full link to the csv file containing your dataset
creditcard_df = pd.read_csv('marketing_data.csv')

# CUSTID: Identification of Credit Card holder 
# BALANCE: Balance amount left in customer's account to make purchases
# BALANCE_FREQUENCY: How frequently the Balance is updated, score between 0 and 1 (1 = frequently updated, 0 = not frequently updated)
# PURCHASES: Amount of purchases made from account
# ONEOFFPURCHASES: Maximum purchase amount done in one-go
# INSTALLMENTS_PURCHASES: Amount of purchase done in installment
# CASH_ADVANCE: Cash in advance given by the user
# PURCHASES_FREQUENCY: How frequently the Purchases are being made, score between 0 and 1 (1 = frequently purchased, 0 = not frequently purchased)
# ONEOFF_PURCHASES_FREQUENCY: How frequently Purchases are happening in one-go (1 = frequently purchased, 0 = not frequently purchased)
# PURCHASES_INSTALLMENTS_FREQUENCY: How frequently purchases in installments are being done (1 = frequently done, 0 = not frequently done)
# CASH_ADVANCE_FREQUENCY: How frequently the cash in advance being paid
# CASH_ADVANCE_TRX: Number of Transactions made with "Cash in Advance"
# PURCHASES_TRX: Number of purchase transactions made
# CREDIT_LIMIT: Limit of Credit Card for user
# PAYMENTS: Amount of Payment done by user
# MINIMUM_PAYMENTS: Minimum amount of payments made by user  
# PRC_FULL_PAYMENT: Percent of full payment paid by user
# TENURE: Tenure of credit card service for user
In [87]:
creditcard_df
Out[87]:
CUST_ID BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
0 C10001 40.900749 0.818182 95.40 0.00 95.40 0.000000 0.166667 0.000000 0.083333 0.000000 0 2 1000.0 201.802084 139.509787 0.000000 12
1 C10002 3202.467416 0.909091 0.00 0.00 0.00 6442.945483 0.000000 0.000000 0.000000 0.250000 4 0 7000.0 4103.032597 1072.340217 0.222222 12
2 C10003 2495.148862 1.000000 773.17 773.17 0.00 0.000000 1.000000 1.000000 0.000000 0.000000 0 12 7500.0 622.066742 627.284787 0.000000 12
3 C10004 1666.670542 0.636364 1499.00 1499.00 0.00 205.788017 0.083333 0.083333 0.000000 0.083333 1 1 7500.0 0.000000 NaN 0.000000 12
4 C10005 817.714335 1.000000 16.00 16.00 0.00 0.000000 0.083333 0.083333 0.000000 0.000000 0 1 1200.0 678.334763 244.791237 0.000000 12
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
8945 C19186 28.493517 1.000000 291.12 0.00 291.12 0.000000 1.000000 0.000000 0.833333 0.000000 0 6 1000.0 325.594462 48.886365 0.500000 6
8946 C19187 19.183215 1.000000 300.00 0.00 300.00 0.000000 1.000000 0.000000 0.833333 0.000000 0 6 1000.0 275.861322 NaN 0.000000 6
8947 C19188 23.398673 0.833333 144.40 0.00 144.40 0.000000 0.833333 0.000000 0.666667 0.000000 0 5 1000.0 81.270775 82.418369 0.250000 6
8948 C19189 13.457564 0.833333 0.00 0.00 0.00 36.558778 0.000000 0.000000 0.000000 0.166667 2 0 500.0 52.549959 55.755628 0.250000 6
8949 C19190 372.708075 0.666667 1093.25 1093.25 0.00 127.040008 0.666667 0.666667 0.000000 0.333333 2 23 1200.0 63.165404 88.288956 0.000000 6

8950 rows × 18 columns

In [88]:
creditcard_df.info()
# 18 features with 8950 points  

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8950 entries, 0 to 8949
Data columns (total 18 columns):
 #   Column                            Non-Null Count  Dtype  
---  ------                            --------------  -----  
 0   CUST_ID                           8950 non-null   object 
 1   BALANCE                           8950 non-null   float64
 2   BALANCE_FREQUENCY                 8950 non-null   float64
 3   PURCHASES                         8950 non-null   float64
 4   ONEOFF_PURCHASES                  8950 non-null   float64
 5   INSTALLMENTS_PURCHASES            8950 non-null   float64
 6   CASH_ADVANCE                      8950 non-null   float64
 7   PURCHASES_FREQUENCY               8950 non-null   float64
 8   ONEOFF_PURCHASES_FREQUENCY        8950 non-null   float64
 9   PURCHASES_INSTALLMENTS_FREQUENCY  8950 non-null   float64
 10  CASH_ADVANCE_FREQUENCY            8950 non-null   float64
 11  CASH_ADVANCE_TRX                  8950 non-null   int64  
 12  PURCHASES_TRX                     8950 non-null   int64  
 13  CREDIT_LIMIT                      8949 non-null   float64
 14  PAYMENTS                          8950 non-null   float64
 15  MINIMUM_PAYMENTS                  8637 non-null   float64
 16  PRC_FULL_PAYMENT                  8950 non-null   float64
 17  TENURE                            8950 non-null   int64  
dtypes: float64(14), int64(3), object(1)
memory usage: 1.2+ MB

MINI CHALLENGE #1:

  • What is the average, minimum and maximum "BALANCE" amount?
In [ ]:
In [89]:
creditcard_df.describe()
# Mean balance is $1564 
# Balance frequency is frequently updated on average ~0.9
# Purchases average is $1000
# one off purchase average is ~$600
# Average purchases frequency is around 0.5
# average ONEOFF_PURCHASES_FREQUENCY, PURCHASES_INSTALLMENTS_FREQUENCY, and CASH_ADVANCE_FREQUENCY are generally low
# Average credit limit ~ 4500
# Percent of full payment is 15%
# Average tenure is 11 years
Out[89]:
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
count 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8950.000000 8949.000000 8950.000000 8637.000000 8950.000000 8950.000000
mean 1564.474828 0.877271 1003.204834 592.437371 411.067645 978.871112 0.490351 0.202458 0.364437 0.135144 3.248827 14.709832 4494.449450 1733.143852 864.206542 0.153715 11.517318
std 2081.531879 0.236904 2136.634782 1659.887917 904.338115 2097.163877 0.401371 0.298336 0.397448 0.200121 6.824647 24.857649 3638.815725 2895.063757 2372.446607 0.292499 1.338331
min 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 50.000000 0.000000 0.019163 0.000000 6.000000
25% 128.281915 0.888889 39.635000 0.000000 0.000000 0.000000 0.083333 0.000000 0.000000 0.000000 0.000000 1.000000 1600.000000 383.276166 169.123707 0.000000 12.000000
50% 873.385231 1.000000 361.280000 38.000000 89.000000 0.000000 0.500000 0.083333 0.166667 0.000000 0.000000 7.000000 3000.000000 856.901546 312.343947 0.000000 12.000000
75% 2054.140036 1.000000 1110.130000 577.405000 468.637500 1113.821139 0.916667 0.300000 0.750000 0.222222 4.000000 17.000000 6500.000000 1901.134317 825.485459 0.142857 12.000000
max 19043.138560 1.000000 49039.570000 40761.250000 22500.000000 47137.211760 1.000000 1.000000 1.000000 1.500000 123.000000 358.000000 30000.000000 50721.483360 76406.207520 1.000000 12.000000

MINI CHALLENGE #2:

  • Obtain the features (row) of the customer who made the maximim "ONEOFF_PURCHASES"
  • Obtain the features of the customer who made the maximum cash advance transaction? how many cash advance transactions did that customer make? how often did he/she pay their bill?
In [ ]:
In [ ]:

TASK #3: VISUALIZE AND EXPLORE DATASET¶

In [90]:
# Let's see if we have any missing data, luckily we don't have many!
sns.heatmap(creditcard_df.isnull(), yticklabels = False, cbar = False, cmap="Blues")
Out[90]:
<AxesSubplot:>
In [91]:
creditcard_df.isnull().sum()
Out[91]:
CUST_ID                               0
BALANCE                               0
BALANCE_FREQUENCY                     0
PURCHASES                             0
ONEOFF_PURCHASES                      0
INSTALLMENTS_PURCHASES                0
CASH_ADVANCE                          0
PURCHASES_FREQUENCY                   0
ONEOFF_PURCHASES_FREQUENCY            0
PURCHASES_INSTALLMENTS_FREQUENCY      0
CASH_ADVANCE_FREQUENCY                0
CASH_ADVANCE_TRX                      0
PURCHASES_TRX                         0
CREDIT_LIMIT                          1
PAYMENTS                              0
MINIMUM_PAYMENTS                    313
PRC_FULL_PAYMENT                      0
TENURE                                0
dtype: int64
In [92]:
# Fill up the missing elements with mean of the 'MINIMUM_PAYMENT' 
creditcard_df.loc[(creditcard_df['MINIMUM_PAYMENTS'].isnull() == True), 'MINIMUM_PAYMENTS'] = creditcard_df['MINIMUM_PAYMENTS'].mean()

MINI CHALLENGE #3:

  • Fill out missing elements in the "CREDIT_LIMIT" column
  • Double check and make sure that no missing elements are present
In [93]:
creditcard_df.loc[(creditcard_df['CREDIT_LIMIT'].isnull() == True), 'CREDIT_LIMIT'] = creditcard_df['CREDIT_LIMIT'].mean()
sns.heatmap(creditcard_df.isnull(), yticklabels = False, cbar = False, cmap="Blues")
Out[93]:
<AxesSubplot:>
In [ ]:
In [94]:
# Let's see if we have duplicated entries in the data
creditcard_df.duplicated().sum()
Out[94]:
0

MINI CHALLENGE #4:

  • Drop Customer ID column 'CUST_ID' and make sure that the column has been removed from the dataframe
In [95]:
# Let's drop Customer ID since it has no meaning here 
creditcard_df.drop("CUST_ID", axis = 1, inplace= True)
creditcard_df.head()
Out[95]:
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
0 40.900749 0.818182 95.40 0.00 95.4 0.000000 0.166667 0.000000 0.083333 0.000000 0 2 1000.0 201.802084 139.509787 0.000000 12
1 3202.467416 0.909091 0.00 0.00 0.0 6442.945483 0.000000 0.000000 0.000000 0.250000 4 0 7000.0 4103.032597 1072.340217 0.222222 12
2 2495.148862 1.000000 773.17 773.17 0.0 0.000000 1.000000 1.000000 0.000000 0.000000 0 12 7500.0 622.066742 627.284787 0.000000 12
3 1666.670542 0.636364 1499.00 1499.00 0.0 205.788017 0.083333 0.083333 0.000000 0.083333 1 1 7500.0 0.000000 864.206542 0.000000 12
4 817.714335 1.000000 16.00 16.00 0.0 0.000000 0.083333 0.083333 0.000000 0.000000 0 1 1200.0 678.334763 244.791237 0.000000 12
In [ ]:
In [96]:
n = len(creditcard_df.columns)
n
Out[96]:
17
In [97]:
creditcard_df.columns
Out[97]:
Index(['BALANCE', 'BALANCE_FREQUENCY', 'PURCHASES', 'ONEOFF_PURCHASES',
       'INSTALLMENTS_PURCHASES', 'CASH_ADVANCE', 'PURCHASES_FREQUENCY',
       'ONEOFF_PURCHASES_FREQUENCY', 'PURCHASES_INSTALLMENTS_FREQUENCY',
       'CASH_ADVANCE_FREQUENCY', 'CASH_ADVANCE_TRX', 'PURCHASES_TRX',
       'CREDIT_LIMIT', 'PAYMENTS', 'MINIMUM_PAYMENTS', 'PRC_FULL_PAYMENT',
       'TENURE'],
      dtype='object')
In [98]:
# distplot combines the matplotlib.hist function with seaborn kdeplot()
# KDE Plot represents the Kernel Density Estimate
# KDE is used for visualizing the Probability Density of a continuous variable. 
# KDE demonstrates the probability density at different values in a continuous variable. 

# Mean of balance is $1500
# 'Balance_Frequency' for most customers is updated frequently ~1
# For 'PURCHASES_FREQUENCY', there are two distinct group of customers
# For 'ONEOFF_PURCHASES_FREQUENCY' and 'PURCHASES_INSTALLMENT_FREQUENCY' most users don't do one off puchases or installment purchases frequently 
# Very small number of customers pay their balance in full 'PRC_FULL_PAYMENT'~0
# Credit limit average is around $4500
# Most customers are ~11 years tenure

plt.figure(figsize=(10,50))
for i in range(len(creditcard_df.columns)):
  plt.subplot(17, 1, i+1)
  sns.distplot(creditcard_df[creditcard_df.columns[i]], kde_kws={"color": "b", "lw": 3, "label": "KDE"}, hist_kws={"color": "g"})
  plt.title(creditcard_df.columns[i])

plt.tight_layout()

C:\Users\SOMNATH\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\SOMNATH\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\SOMNATH\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\SOMNATH\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\SOMNATH\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\SOMNATH\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\SOMNATH\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\SOMNATH\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\SOMNATH\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\SOMNATH\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\SOMNATH\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\SOMNATH\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\SOMNATH\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\SOMNATH\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\SOMNATH\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\SOMNATH\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)
C:\Users\SOMNATH\anaconda3\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

MINI CHALLENGE #5:

  • Obtain the correlation matrix between features
In [99]:
correlations = creditcard_df.corr()
f, ax = plt.subplots(figsize = (20, 20))
sns.heatmap(correlations, annot = True)

# 'PURCHASES' have high correlation between one-off purchases, 'installment purchases, purchase transactions, credit limit and payments. 
# Strong Positive Correlation between 'PURCHASES_FREQUENCY' and 'PURCHASES_INSTALLMENT_FREQUENCY'
Out[99]:
<AxesSubplot:>

TASK #4: UNDERSTAND THE THEORY AND INTUITON BEHIND K-MEANS¶

image.png

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image.png

MINI CHALLENGE #6:

  • Which of the following conditions could terminate the K-means clustering algorithm? (choose 2)
    • K-means terminates after a fixed number of iterations is reached
    • K-means terminates when the number of clusters does not increase between iterations
    • K-means terminates when the centroid locations do not change between iterations
In [ ]:

TASK #5: LEARN HOW TO OBTAIN THE OPTIMAL NUMBER OF CLUSTERS (ELBOW METHOD)¶

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TASK #6: FIND THE OPTIMAL NUMBER OF CLUSTERS USING ELBOW METHOD¶

  • The elbow method is a heuristic method of interpretation and validation of consistency within cluster analysis designed to help find the appropriate number of clusters in a dataset.
  • If the line chart looks like an arm, then the "elbow" on the arm is the value of k that is the best.
  • Source:
    • https://en.wikipedia.org/wiki/Elbow_method_(clustering)
    • https://www.geeksforgeeks.org/elbow-method-for-optimal-value-of-k-in-kmeans/
In [100]:
# Let's scale the data first
scaler = StandardScaler()
creditcard_df_scaled = scaler.fit_transform(creditcard_df)
In [101]:
creditcard_df_scaled.shape
Out[101]:
(8950, 17)
In [102]:
creditcard_df_scaled
Out[102]:
array([[-0.73198937, -0.24943448, -0.42489974, ..., -0.31096755,
        -0.52555097,  0.36067954],
       [ 0.78696085,  0.13432467, -0.46955188, ...,  0.08931021,
         0.2342269 ,  0.36067954],
       [ 0.44713513,  0.51808382, -0.10766823, ..., -0.10166318,
        -0.52555097,  0.36067954],
       ...,
       [-0.7403981 , -0.18547673, -0.40196519, ..., -0.33546549,
         0.32919999, -4.12276757],
       [-0.74517423, -0.18547673, -0.46955188, ..., -0.34690648,
         0.32919999, -4.12276757],
       [-0.57257511, -0.88903307,  0.04214581, ..., -0.33294642,
        -0.52555097, -4.12276757]])
In [103]:
# Index(['BALANCE', 'BALANCE_FREQUENCY', 'PURCHASES', 'ONEOFF_PURCHASES',
#       'INSTALLMENTS_PURCHASES', 'CASH_ADVANCE', 'PURCHASES_FREQUENCY',
#       'ONEOFF_PURCHASES_FREQUENCY', 'PURCHASES_INSTALLMENTS_FREQUENCY',
#       'CASH_ADVANCE_FREQUENCY', 'CASH_ADVANCE_TRX', 'PURCHASES_TRX',
#       'CREDIT_LIMIT', 'PAYMENTS', 'MINIMUM_PAYMENTS', 'PRC_FULL_PAYMENT',
#       'TENURE'], dtype='object')

scores_1 = []

range_values = range(1, 20)

for i in range_values:
  kmeans = KMeans(n_clusters = i)
  kmeans.fit(creditcard_df_scaled)
  scores_1.append(kmeans.inertia_)

plt.plot(scores_1, 'bx-')
plt.title('Finding the right number of clusters')
plt.xlabel('Clusters')
plt.ylabel('Scores') 
plt.show()

# From this we can observe that, 4th cluster seems to be forming the elbow of the curve. 
# However, the values does not reduce linearly until 8th cluster. 
# Let's choose the number of clusters to be 7 or 8.

C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)

MINI CHALLENGE #7:

  • Let's assume that our data only consists of the first 7 columns of "creditcard_df_scaled", what is the optimal number of clusters would be in this case? modify the code and rerun the cells.
In [ ]:

TASK #7: APPLY K-MEANS METHOD¶

In [104]:
kmeans = KMeans(7)
kmeans.fit(creditcard_df_scaled)
labels = kmeans.labels_

C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
In [105]:
kmeans.cluster_centers_.shape
Out[105]:
(7, 17)
In [106]:
cluster_centers = pd.DataFrame(data = kmeans.cluster_centers_, columns = [creditcard_df.columns])
cluster_centers
Out[106]:
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
0 -0.701882 -2.133384 -0.306746 -0.230358 -0.301974 -0.323199 -0.546483 -0.427927 -0.440794 -0.522378 -0.376899 -0.416139 -0.172939 -0.201852 -0.257584 0.297197 0.200916
1 0.009569 0.401333 -0.343591 -0.223509 -0.401679 -0.101601 -0.814704 -0.334176 -0.759165 0.089260 -0.037753 -0.465883 -0.299640 -0.246055 -0.014953 -0.451697 0.272443
2 1.676448 0.394844 -0.196266 -0.147445 -0.193282 1.997830 -0.447815 -0.185323 -0.396913 1.910432 1.926691 -0.233923 1.014173 0.838548 0.591191 -0.391335 0.068171
3 1.488505 0.403475 7.413638 6.553369 5.486972 0.028557 1.072872 1.875094 1.028264 -0.309559 -0.107750 4.914457 2.259343 5.133194 0.782295 0.828775 0.330386
4 -0.368652 0.333102 -0.041819 -0.231056 0.325438 -0.366980 0.973176 -0.344051 1.161221 -0.476896 -0.362294 0.169743 -0.275188 -0.223685 -0.031324 0.294031 0.254601
5 -0.336090 -0.346701 -0.284230 -0.209208 -0.287346 0.064694 -0.196404 -0.273217 -0.228349 0.302558 -0.002968 -0.385403 -0.557539 -0.390658 -0.209560 0.017471 -3.192867
6 0.143846 0.431066 0.975990 0.923239 0.611536 -0.306680 1.100349 1.877149 0.549683 -0.405291 -0.315473 1.230482 0.719987 0.413877 -0.030239 0.471861 0.307348
In [107]:
# In order to understand what these numbers mean, let's perform inverse transformation
cluster_centers = scaler.inverse_transform(cluster_centers)
cluster_centers = pd.DataFrame(data = cluster_centers, columns = [creditcard_df.columns])
cluster_centers

# First Customers cluster (Transactors): Those are customers who pay least amount of intrerest charges and careful with their money, Cluster with lowest balance ($104) and cash advance ($303), Percentage of full payment = 23%
# Second customers cluster (revolvers) who use credit card as a loan (most lucrative sector): highest balance ($5000) and cash advance (~$5000), low purchase frequency, high cash advance frequency (0.5), high cash advance transactions (16) and low percentage of full payment (3%)
# Third customer cluster (VIP/Prime): high credit limit $16K and highest percentage of full payment, target for increase credit limit and increase spending habits
# Fourth customer cluster (low tenure): these are customers with low tenure (7 years), low balance
Out[107]:
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
0 103.566909 0.371892 347.837146 210.090875 137.996103 301.107446 0.271021 0.074799 0.189255 0.030611 0.676768 4.366162 3865.226805 1148.803276 263.917593 0.240640 11.786195
1 1584.392875 0.972343 269.118389 221.458752 47.834075 765.809207 0.163370 0.102766 0.062726 0.153006 2.991188 3.129714 3404.235695 1020.840061 829.358513 0.021601 11.881918
2 5053.860126 0.970806 583.880236 347.709348 236.285253 5168.412968 0.310621 0.147172 0.206694 0.517441 16.397075 8.895388 8184.425810 4160.658322 2241.953121 0.039256 11.608549
3 4662.671853 0.972850 16842.556892 11469.688108 5372.868784 1038.757441 0.920946 0.761835 0.773096 0.073198 2.513514 136.864865 12714.864865 16593.236130 2687.311037 0.396117 11.959459
4 797.157201 0.956179 913.858518 208.932467 705.357378 209.297141 0.880933 0.099821 0.825936 0.039712 0.776437 18.929020 3493.201450 1085.596302 791.206647 0.239714 11.858040
5 864.932877 0.795140 395.943444 245.195746 151.223889 1114.536873 0.411524 0.120952 0.273685 0.195689 3.228571 5.130159 2465.895952 602.226195 375.836775 0.158825 7.244444
6 1863.877866 0.979386 3088.421697 2124.825567 964.071943 335.749646 0.931974 0.762448 0.582895 0.054041 1.095956 45.295004 7114.058107 2931.275785 793.735826 0.291726 11.928628
In [108]:
labels.shape # Labels associated to each data point
Out[108]:
(8950,)
In [109]:
labels.max()
Out[109]:
6
In [110]:
labels.min()
Out[110]:
0
In [111]:
y_kmeans = kmeans.fit_predict(creditcard_df_scaled)
y_kmeans

C:\Users\SOMNATH\anaconda3\lib\site-packages\sklearn\cluster\_kmeans.py:1412: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning
  super()._check_params_vs_input(X, default_n_init=10)
Out[111]:
array([2, 0, 4, ..., 5, 5, 5])
In [112]:
# concatenate the clusters labels to our original dataframe
creditcard_df_cluster = pd.concat([creditcard_df, pd.DataFrame({'cluster':labels})], axis = 1)
creditcard_df_cluster.head()
Out[112]:
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE cluster
0 40.900749 0.818182 95.40 0.00 95.4 0.000000 0.166667 0.000000 0.083333 0.000000 0 2 1000.0 201.802084 139.509787 0.000000 12 1
1 3202.467416 0.909091 0.00 0.00 0.0 6442.945483 0.000000 0.000000 0.000000 0.250000 4 0 7000.0 4103.032597 1072.340217 0.222222 12 2
2 2495.148862 1.000000 773.17 773.17 0.0 0.000000 1.000000 1.000000 0.000000 0.000000 0 12 7500.0 622.066742 627.284787 0.000000 12 6
3 1666.670542 0.636364 1499.00 1499.00 0.0 205.788017 0.083333 0.083333 0.000000 0.083333 1 1 7500.0 0.000000 864.206542 0.000000 12 1
4 817.714335 1.000000 16.00 16.00 0.0 0.000000 0.083333 0.083333 0.000000 0.000000 0 1 1200.0 678.334763 244.791237 0.000000 12 1
In [113]:
# Plot the histogram of various clusters
for i in creditcard_df.columns:
  plt.figure(figsize = (35, 5))
  for j in range(7):
    plt.subplot(1,7,j+1)
    cluster = creditcard_df_cluster[creditcard_df_cluster['cluster'] == j]
    cluster[i].hist(bins = 20)
    plt.title('{}    \nCluster {} '.format(i,j))
  
  plt.show()

MINI CHALLENGE #8:

  • Repeat the same procedure with 8 clusters instead of 7

TASK 8: APPLY PRINCIPAL COMPONENT ANALYSIS AND VISUALIZE THE RESULTS¶

image.png

In [114]:
# Obtain the principal components 
pca = PCA(n_components=2)
principal_comp = pca.fit_transform(creditcard_df_scaled)
principal_comp
Out[114]:
array([[-1.6822218 , -1.07644896],
       [-1.13829518,  2.50647535],
       [ 0.96968661, -0.38352372],
       ...,
       [-0.92620587, -1.81078233],
       [-2.336554  , -0.65796235],
       [-0.55641851, -0.40047109]])
In [115]:
# Create a dataframe with the two components
pca_df = pd.DataFrame(data = principal_comp, columns =['pca1','pca2'])
pca_df.head()
Out[115]:
pca1 pca2
0 -1.682222 -1.076449
1 -1.138295 2.506475
2 0.969687 -0.383524
3 -0.873628 0.043165
4 -1.599436 -0.688578
In [116]:
# Concatenate the clusters labels to the dataframe
pca_df = pd.concat([pca_df,pd.DataFrame({'cluster':labels})], axis = 1)
pca_df.head()
Out[116]:
pca1 pca2 cluster
0 -1.682222 -1.076449 1
1 -1.138295 2.506475 2
2 0.969687 -0.383524 6
3 -0.873628 0.043165 1
4 -1.599436 -0.688578 1
In [117]:
plt.figure(figsize=(10,10))
ax = sns.scatterplot(x="pca1", y="pca2", hue = "cluster", data = pca_df, palette =['red','green','blue','pink','yellow','gray','purple'])
plt.show()

MINI CHALLENGE #9:

  • Repeat task #7 and #8 with number of clusters = 7 and 4

EXCELLENT JOB! YOU SHOULD BE PROUD OF YOUR NEWLY ACQUIRED SKILLS¶

MINI CHALLENGE SOLUTIONS

MINI CHALLENGE #1

In [76]:
# Average, minimum and maximum balance amounts
print('The average, minimum and maximum balance amount are:', creditcard_df['BALANCE'].mean(), creditcard_df['BALANCE'].min(), creditcard_df['BALANCE'].max())

The average, minimum and maximum balance amount are: 1564.4748276781038 0.0 19043.13856

MINI CHALLENGE #2

In [77]:
# Let's see who made one off purchase of $40761!
creditcard_df[creditcard_df['ONEOFF_PURCHASES'] == 40761.25]
Out[77]:
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
550 11547.52001 1.0 49039.57 40761.25 8278.32 558.166886 1.0 1.0 0.916667 0.083333 1 101 22500.0 46930.59824 2974.069421 0.25 12
In [78]:
creditcard_df['CASH_ADVANCE'].max()
Out[78]:
47137.21176
In [80]:
# Let's see who made cash advance of $47137!
# This customer made 123 cash advance transactions!!
# Never paid credit card in full

creditcard_df[creditcard_df['CASH_ADVANCE'] == 47137.21176]
Out[80]:
BALANCE BALANCE_FREQUENCY PURCHASES ONEOFF_PURCHASES INSTALLMENTS_PURCHASES CASH_ADVANCE PURCHASES_FREQUENCY ONEOFF_PURCHASES_FREQUENCY PURCHASES_INSTALLMENTS_FREQUENCY CASH_ADVANCE_FREQUENCY CASH_ADVANCE_TRX PURCHASES_TRX CREDIT_LIMIT PAYMENTS MINIMUM_PAYMENTS PRC_FULL_PAYMENT TENURE
2159 10905.05381 1.0 431.93 133.5 298.43 47137.21176 0.583333 0.25 0.5 1.0 123 21 19600.0 39048.59762 5394.173671 0.0 12

MINI CHALLENGE #3

In [81]:
# Fill up the missing elements with mean of the 'CREDIT_LIMIT' 
creditcard_df.loc[(creditcard_df['CREDIT_LIMIT'].isnull() == True), 'CREDIT_LIMIT'] = creditcard_df['CREDIT_LIMIT'].mean()
sns.heatmap(creditcard_df.isnull(), yticklabels = False, cbar = False, cmap="Blues")
Out[81]:
<AxesSubplot:>

MINI CHALLENGE #4

In [ ]:
# Let's drop Customer ID since it has no meaning here 
creditcard_df.drop("CUST_ID", axis = 1, inplace= True)
creditcard_df.head()

MINI CHALLENGE #5

In [ ]:
correlations = creditcard_df.corr()
f, ax = plt.subplots(figsize = (20, 20))
sns.heatmap(correlations, annot = True)

# 'PURCHASES' have high correlation between one-off purchases, 'installment purchases, purchase transactions, credit limit and payments. 
# Strong Positive Correlation between 'PURCHASES_FREQUENCY' and 'PURCHASES_INSTALLMENT_FREQUENCY'

MINI CHALLENGE #6:

  • Which of the following conditions could terminate the K-means clustering algorithm? (choose 2)
    • K-means terminates after a fixed number of iterations is reached (True)
    • K-means terminates when the number of clusters does not increase between iterations (False)
    • K-means terminates when the centroid locations do not change between iterations (True)

MINI CHALLENGE #7:

In [ ]:
# code modification
kmeans.fit(creditcard_df_scaled[:7])
# optimal number of clusters would be = 3

MINI CHALLENGE #8 & #9:

  • simply change the values requested in the question and rerun the cells